Dean J A, Welsh L C, Wong K H, Aleksic A, Dunne E, Islam M R, Patel A, Patel P, Petkar I, Phillips I, Sham J, Schick U, Newbold K L, Bhide S A, Harrington K J, Nutting C M, Gulliford S L
Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
Head and Neck Unit, The Royal Marsden NHS Foundation Trust, London, UK.
Clin Oncol (R Coll Radiol). 2017 Apr;29(4):263-273. doi: 10.1016/j.clon.2016.12.001. Epub 2017 Jan 3.
A normal tissue complication probability (NTCP) model of severe acute mucositis would be highly useful to guide clinical decision making and inform radiotherapy planning. We aimed to improve upon our previous model by using a novel oral mucosal surface organ at risk (OAR) in place of an oral cavity OAR.
Predictive models of severe acute mucositis were generated using radiotherapy dose to the oral cavity OAR or mucosal surface OAR and clinical data. Penalised logistic regression and random forest classification (RFC) models were generated for both OARs and compared. Internal validation was carried out with 100-iteration stratified shuffle split cross-validation, using multiple metrics to assess different aspects of model performance. Associations between treatment covariates and severe mucositis were explored using RFC feature importance.
Penalised logistic regression and RFC models using the oral cavity OAR performed at least as well as the models using mucosal surface OAR. Associations between dose metrics and severe mucositis were similar between the mucosal surface and oral cavity models. The volumes of oral cavity or mucosal surface receiving intermediate and high doses were most strongly associated with severe mucositis.
The simpler oral cavity OAR should be preferred over the mucosal surface OAR for NTCP modelling of severe mucositis. We recommend minimising the volume of mucosa receiving intermediate and high doses, where possible.
严重急性粘膜炎的正常组织并发症概率（NTCP）模型对于指导临床决策和放疗计划制定非常有用。我们旨在通过使用一种新型的口腔粘膜表面危及器官（OAR）替代口腔OAR来改进我们之前的模型。
利用口腔OAR或粘膜表面OAR的放疗剂量及临床数据建立严重急性粘膜炎的预测模型。针对两种OAR分别生成惩罚逻辑回归模型和随机森林分类（RFC）模型并进行比较。采用100次迭代分层随机分割交叉验证进行内部验证，使用多个指标评估模型性能的不同方面。利用RFC特征重要性探索治疗协变量与严重粘膜炎之间的关联。
使用口腔OAR的惩罚逻辑回归模型和RFC模型的表现至少与使用粘膜表面OAR的模型一样好。粘膜表面模型和口腔模型中剂量指标与严重粘膜炎之间的关联相似。接受中等剂量和高剂量的口腔或粘膜表面体积与严重粘膜炎的相关性最强。
在严重粘膜炎的NTCP建模中，应优先选择更简单的口腔OAR而非粘膜表面OAR。我们建议尽可能减少接受中等剂量和高剂量的粘膜体积。
